Hyperspectral data visualizations are useful as a background layer to labeling information in the hyperspectral scene such as classification information, locations, or geographic features. Given a hyperspectral image H , where the ith-jth pixel Hij is a d-dimensional vector representing reflectance at d wavelengths, any dimensionality-reduction method an be used to reduce the d dimensions down ...

Hyperspectral images (HSIs) has become very popular area of research. This paper deals with the compression and classification of Hyperspectral images using Discrete Wavelet Technique in conjunction with Non negative Tucker Decomposition. This algorithm exploits both the spectral and the spatial information of the images. The core idea behind the proposed technique is to apply TD on the DWT coe...

Remote sensing has become an important source of urban land-use/cover classification, and as a result of their high spatial and spectral resolution, airborne hyperspectral images have been widely used to distinguish different urban classes. However, the previous studies into the classification of urban environments have mainly focused on a supervised scenario, which is limited by the selection ...

Hyperspectral cameras preserve the fine spectral details of scenes that are generally lost in the traditional RGB cameras due to the gross quantization of radiance. These details are desirable in numerous imaging applications, nevertheless the high cost of hyperspectral hardware and the associated physical constraints currently limit the pervasive use of hyperspectral imaging. We take a computa...

This paper presents a new kernel framework for hyperspectral images classification. In this paper, a new feature extraction algorithm based on wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method can improve the nonlinear mapping capability of kernel non-negative matrix fac...

In this paper we propose an analysis on the joint effect of hyperspectral and LIDARdata for the classification of complex forest areas. In greater detail, we describe an advanced system for LI-DAR and hyperspectral image analysis and investigate the effectiveness of Support Vector Ma-chine and Gaussian Maximum Likelihood (with Leave-One-Out-Covariance estimation) classifi-cation techniques in c...

To visualize and manipulate musical signals time-frequency transforms have been used extensively. The Large Time Frequency Analysis Toolbox is an Octave/Matlab toolbox for modern signal analysis and synthesis. The toolbox provides a large variety of linear and invertible time-frequency transforms like Gabor, MDCT, constant-Q, filterbanks and wavelets transforms, and routines for modifying music...

Background: Hyperspectral image analysis is a fast and non-destructive technique that is being used to measure quality attributes of food products. This research investigated the feasibility of predicting internal quality attributes, such as Total Soluble Solids (TSS), pH, Titratable Acidity (TA), and maturity index (TSS/TA); and external quality attributes such as color components (L*, a*, b*)...

Copyright © 2013 Heesung Kwon et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent advances in multispectral and hyperspectral sensing technologies coupled with rapid growth in computing power have led to new opportunities...

SUMMARY We introduce a novel Matlab toolbox for microarray data analysis. This toolbox uses normalization based upon a normally distributed background and differential gene expression based on five statistical measures. The objects in this toolbox are open source and can be implemented to suit your application. AVAILABILITY MDAT v1.0 is a Matlab toolbox and requires Matlab to run. MDAT is fre...